1. Technical Field
The invention relates to optical sampling of tissue in-vivo. More particularly, the invention relates to an optical sampling interface system that includes an optical probe placement guide, a means for stabilizing the sampled tissue, an optical coupler for repeatably sampling a tissue measurement site in-vivo, and/or a means for compensating for measurement bias.
2. Description of the Prior Art
In-vivo measurement of tissue properties and analytes using optical based analyzers requires that a tissue measurement region be positioned and coupled with respect to an optical interface or probe. The requirements of an optical sampling interface system for such placement and coupling depends upon the nature of the tissue properties and analytes under consideration, the optical technology being applied, and the variability of the tissue with respect to the target analyte. Often, when sampling reproducibility is vital, the optical measurement is performed in a laboratory where the majority of the factors pertaining to the measurement are controlled or constrained. However, there are many demanding in-vivo applications that cannot be performed in a laboratory setting, but yet require a high degree of optical sampling reproducibility. In one example, a relatively unskilled operator or user must perform the optical measurement. One such application is the noninvasive estimation of glucose concentration through near-infrared spectroscopy. With the desired end result being an optical measurement system that is used by the consumer in a variety of environments, the optical sampling requirements are stringent. This problem is further considered through a discussion of the target application, the structure of live skin, and the dynamic properties of live tissue.
Diabetes
Diabetes is a chronic disease that results in abnormal production and use of insulin, a hormone that facilitates glucose uptake into cells. While a precise cause of diabetes is unknown, genetic factors, environmental factors, and obesity play roles. Diabetics have increased risk in three broad categories: cardiovascular heart disease, retinopathy, and neuropathy. Diabetics often have one or more of the following complications: heart disease and stroke, high blood pressure, kidney disease, neuropathy (nerve disease and amputations), retinopathy, diabetic ketoacidosis, skin conditions, gum disease, impotence, and fetal complications. Diabetes is a leading cause of death and disability worldwide. Moreover, diabetes is merely one among a group of disorders of glucose metabolism that also includes impaired glucose tolerance and hyperinsulinemia, which is also known as hypoglycemia.
Diabetes Prevalence and Trends
The prevalence of individuals with diabetes is increasing with time. The World Health Organization (WHO) estimates that diabetes currently afflicts 154 million people worldwide. There are 54 million people with diabetes living in developed countries. The WHO estimates that the number of people with diabetes will grow to 300 million by the year 2025. In the United States, 15.7 million people or 5.9 percent of the population are estimated to have diabetes. Within the United States, the prevalence of adults diagnosed with diabetes increased by 6% in 1999 and rose by 33% between 1990 and 1998. This corresponds to approximately eight hundred thousand new cases every year in America. The estimated total cost to the United States economy alone exceeds $90 billion per year. Diabetes Statistics, National Institutes of Health, Publication No. 98-3926, Bethesda, Md. (November 1997).
Noninvasive Estimation of Glucose Concentration
Numerous approaches have been explored for estimating blood glucose concentrations, ranging from invasive methods such as microdialysis to noninvasive technologies that rely on spectroscopy. Each method has associated advantages and disadvantages, but only a few have received approval from certifying agencies. To date, no noninvasive techniques for the self-monitoring of blood glucose concentration have been certified by the U.S. Food and Drug Administration.
One method, near-infrared spectroscopy, involves the illumination of a region of the body with near-infrared electromagnetic radiation, i.e. light in the wavelength range 700 to 2500 nm. The light is partially absorbed and scattered, according to its interaction with the tissue constituents prior to being reflected back to a detector. The detected light contains quantitative information that is based on the known interaction of the incident light with components of the body tissue including water, fat, protein, and glucose.
Previously reported methods for the noninvasive estimation of glucose concentration through near-infrared spectroscopy rely on the detection of the magnitude of light attenuation caused by the absorption signature of blood glucose as represented in the targeted tissue volume. The targeted tissue volume is that portion of irradiated tissue from which light is reflected or transmitted to the spectrometer detection system. The signal due to the absorption of glucose is extracted from the spectral measurement through various methods of signal processing and one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary set of spectral measurements and associated reference blood glucose concentrations (the calibration set) based on an analysis of capillary (fingertip) blood, venous blood, and/or alternative site fluids.
Near-infrared spectroscopy has been demonstrated in specific studies to represent a possible approach for the noninvasive measurement of blood glucose levels. M. Robinson, R. Eaton, D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasive glucose monitoring in diabetic patients: A preliminary evaluation, Clin Chem, 38: 1618-22 (1992) reports three different instrument configurations for measuring diffuse transmittance through the finger in the 600-1300 nm range. Meal tolerance tests were used to perturb the glucose concentrations of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation. Absolute average prediction errors ranged from 19.8 to 37.8 mg/dL. H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasive blood glucose sensors based on near-infrared spectroscopy, Artif Org, 18: 439-47 (1994); H. Heise, R. Marbach, Effect of data pretreatment on the noninvasive blood glucose measurement by diffuse reflectance near-IR spectroscopy, SPIE Proc, 2089: 114-5 (1994); R. Marbach, T. Koschinsky, F. Gries, H. Heise, Noninvasive glucose assay by near-infrared diffuse reflectance spectroscopy of the human inner lip, Appl Spectrosc, 47: 875-81 (1993); and R. Marbach, H. Heise, Optical diffuse reflectance accessory for measurements of skin tissue by near-infrared spectroscopy, Applied Optics 34(4): 610-21 (1995) present results through a diffuse reflectance measurement of the oral mucosa in the 1111 to 1835 nm range with an optimized diffuse reflectance accessory. In-vivo experiments were conducted on single diabetics using glucose tolerance tests and on a population of 133 different subjects. The best standard error of estimation reported was 43 mg/dL and was obtained from a two-day single person oral glucose tolerance test that was evaluated through cross-validation.
K. Jagemann, C. Fischbacker, K. Danzer, U. Muller, B. Mertes, Application of near-infrared spectroscopy for noninvasive determination of blood/tissue glucose using neural network, Z Phys Chem, 191S: 179-190 (1995); C. Fischbacker, K. Jagemann, K. Danzer, U. Muller, L. Papenkrodt, J. Schuler, Enhancing calibration models for noninvasive near-infrared spectroscopic blood glucose determinations, Fresenius J Anal Chem 359: 78-82 (1997); K. Danzer, C. Fischbacker, K. Jagemann, K. Reichelt, Near-infrared diffuse reflection spectroscopy for noninvasive blood-glucose monitoring, LEOS Newsletter 12(2): 9-11 (1998); and U. Muller, B. Mertes, C. Fischbacker, K. Jagemann, K. Danzer, Noninvasive blood glucose monitoring by means of new infrared spectroscopic methods for improving the reliability of the calibration models, Int J Artif Organs, 20: 285-290 (1997) recorded spectra in diffuse reflectance over the 800 to 1350 nm range on the middle finger of the right hand with a fiber-optic probe. Each experiment involved a diabetic subject and was conducted over a single day with perturbation of blood glucose concentrations through carbohydrate loading. Results, using both partial least squares regression and radial basis function neural networks, were evaluated on single subjects over single days through cross-validation. Danzer, et al., supra, report an average root mean square measurement error of 36 mg/dL through cross-validation over 31 glucose concentration profiles.
J. Burmeister, M. Arnold, G. Small, Human noninvasive measurement of glucose using near infrared spectroscopy [abstract], Pittcon, New Orleans La. (1998) collected absorbance spectra through a transmission measurement of the tongue in the 1429 to 2000 nm range. A study of five diabetic subjects was conducted over a 39-day period with five samples taken per day. Every fifth sample was used for an independent test set and the standard error of estimation for all subjects was greater than 54 mg/dL.
T. Blank, T. Ruchti, S. Malin, S. Monfre, The use of near-infrared diffuse reflectance for the noninvasive prediction of blood glucose, IEEE Lasers and Electro-Optics Society Newsletter, 13: 5 (October 1999), report studies that demonstrate noninvasive estimation of blood glucose concentration during modified oral glucose tolerance tests over a short time period. The calibration was customized for the individual and tested over a relatively short time period.
In all of these studies, diverse limitations were cited that affect the acceptance of such a method as a commercial product. Fundamental to all the studies is the problem of the small signal attributable to glucose, particularly in view of the difficulty in obtaining a reproducible sample of a given tissue volume, as a result of the complex and dynamic nature of the tissue. For example, see O. Khalil, Spectroscopic and clinical aspects of noninvasive glucose measurements, Clin Chem, v. 45, pp. 165-77 (1999). The sampling problem is further accentuated by noting that the reported studies were performed under highly controlled conditions using skilled professionals rather than in a home environment by the consumer. As reported by S. Malin, T. Ruchti, An Intelligent System for Noninvasive Blood Analyte Prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001), the entirety of which is hereby incorporated by reference, chemical, structural and physiological variations occur that produce dramatic and nonlinear changes in the optical properties of the tissue sample. See R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, 7:1, pp. 13-19 (1981); W. Cheong, S. Prahl, A Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, 26:12, pp. 2166-2185, (December 1990); D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue using time-resolved spectrophotometry: the impact of statically and dynamically variable optical path lengths, SPIE, 1888, pp. 10-21 (1993); J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, 40, pp. 1123-1140 (December 1984); S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, 1:4, pp. 418-424 (October 1996); A Profio, Light transport in tissue, Applied Optics, 28:12), pp. 2216-2222, (June 1989), M. Van Gemert, S. Jacques, H. Sterenborg, W. Star, Skin optics, IEEE Transactions on Biomedical Engineering, 36:12, pp. 1146-1154 (December 1989); and B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, 26:12, pp. 2186-2199.
The measurement is further complicated by the heterogeneity of the sample, the multi-layered structure of the skin, the rapid variation related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, temperature fluctuations, and changes in blood constituent concentrations. This is further considered through a discussion of the scattering properties of skin and the dynamic nature of the tissue.
Structure of Human Skin
The structure and pigmentation of human skin varies widely among individuals, between different sites on the same individual, and within an individual over time. Skin includes stratified layers, a cellular epidermis, and an underlying dermis of connective tissue. Below the dermis is a subcutaneous fatty layer or adipose tissue. The epidermis is the thin outer layer that provides a barrier to infection and loss of moisture, while the dermis is the thick inner layer that provides mechanical strength and elasticity. The epidermis layer is 10 to 150 μm thick and is divided into three layers, the basal, middle, and superficial layers. The basal layer borders the dermis and contains pigment-forming melanocyte cells, keratinocyte cells, Langherhan cells and Merkel cells See F. Ebling, The normal skin, In: Textbook of Dermatology, A Rook, D. Wilkinson, F. Ebling, eds., 3ed., pp. 5-30, Blackwell Scientific Publishers, Oxford, England (1979). An outer superficial layer is also known as the stratum corneum.
The stratum corneum, the outermost layer of the mammalian epidermis, is formed and continuously replenished by the slow upward migration of aqueous keratinocyte cells from the germinative basal layer of the epidermis. It is replenished about every two weeks in mature adults. See W. Montagna, The Structure and Function of Skin, 2ed., p. 454, Academic Press, New York, (1961). This complex process involving intracellular dehydration and synthesis of an insoluble protein, keratin, results in keratin-filled, biologically inactive, shrunken cells. These flat, dehydrated, hexagonal cells are tightly bound to their neighbors and each is approximately 30 μm wide and 0.8 μm deep. See H. Baker, The skin as a barrier, In: Textbook of Dermatology, A. Rook, D. Wilkinson, F. Ebling, eds., 3ed., pp. 5 to 30, Blackwell Scientific Publishers, Oxford, England (1979). There are about twelve to twenty cell layers over most of the body surface. The stratum corneum is typically 10 to 20 μm thick, except on the planar surfaces, where it is considerably thicker. See A Kligman, The Biology of the stratum corneum, in: The Epidermis, W. Montagna, W. Lobitz, eds. Academic Press, New York, pp. 387-433 (1964).
The major constituent of the dermis, apart from water, is a fibrous protein, collagen, which is embedded in a ground substance composed mainly of protein and glycosaminoglycans. The glycosaminoglycans play a key role in regulating the assembly of collagen fibrils and tissue permeability to water and other molecules. See K. Trier, S. Olsen, T. Ammitzboll, Acta. Ophthalmol., v. 69, pp. 304 to 306 (1990). Collagen is the most abundant protein in the human body. Elastin fibers are also plentiful though they constitute a smaller proportion of the bulk. The dermis also contains other cellular constituents and has a very rich blood supply, though no vessels pass the dermo-epidermal junction. See Ebling, supra. The blood vessels nourish the skin and control body temperature. In humans, the thickness of the dermis ranges from 0.5 mm over the eyelid to 4 mm on the back and has an average thickness of approximately 1.2 mm over most of the body. See S. Wilson, V. Spence, Phys. Med. Biol. v. 33, pp. 894-897 (1988).
The spectral characteristics of water, protein, fat, urea, and glucose are all unique in the near-infrared from 1100 to 2500 nm.
Interaction Between Light and Human Skin
When a beam of light is directed onto the skin surface, a part of it is reflected while the remaining part penetrates into the skin. The proportion of reflected light energy is strongly dependent on the angle of incidence. At nearly perpendicular incidence, about 4% of the incident beam is reflected due to the change in refractive index between air (ηD=1.0) and dry stratum corneum (ηD=1.55). For normally incident radiation, this specular reflectance component may be as high as 7% because the very rigid and irregular surface of the stratum corneum produces off-normal angles of incidence. Regardless of skin color, specular reflectance of a nearly perpendicular beam from normal skin is between four percent and seven percent over the entire spectrum from 250 to 3000 nm. See R. Scheuplein, J. Soc. Cosmet. Chem., v. 15, pp. 111 to 122 (1964). Only the air-stratum corneum border gives rise to a regular reflection. Indices of refraction of most soft tissue (skin, liver, heart, etc) lie within the 1.38 to 1.41 range with the exception of adipose tissue, which has a refractive index of approximately 1.46. See J. Parrish, R. Anderson, F. Urbach, D. Pitts, UV-A: Biologic effects of ultraviolet radiation with emphasis on human responses to longwave ultraviolet, New York, Plenum Press (1978). The differences are expected to be less significant when the stratum corneum is hydrated, owing to refractive index matching.
The 93 percent to 96 percent of the incident beam that enters the skin is attenuated due to absorption or scattering within any of the layers of the skin. These two processes taken together essentially determine the penetration of light into skin, as well as remittance of scattered light from the skin. A definition of diffuse reflectance or remittance is that fraction of incident optical radiation that is returned from a turbid sample. Absorption by the various skin constituents mentioned above account for the spectral extinction of the beam within each layer. Scattering is the primary process by which the beam is returned to the incident layer. Scattering results from differences in a medium's refractive index, corresponding to differences in the physical characteristics of the particles that make up the medium. The spatial distribution and intensity of scattered light depends upon the size and shape of the particles relative to the wavelength, and upon the difference in refractive index between the medium and the constituent particles.
The scattering coefficient of biological tissue depends on many uncontrollable factors, which include the concentration of interstitial water, the density of structural fibers, and the shapes and sizes of cellular structures. Scattering by collagen fibers is of major importance in determining the penetration of optical radiation within the dermis. See F. Bolin, L. Preuss, R. Taylor, R. Ference, Appl. Opt, v. 28, pp. 2297 to 2303 (1989). The greater the diffusing power of a medium, the greater the absorption related to multiple internal reflections. Therefore, reflectance values measured on different sites on the same person, or from the same site on different people, can differ substantially even when the target absorber is present in the same concentration. These differences are attributed to tissue parameters, such as gender, age, genetics, disease, and exogenous factors due to lifestyle differences. For example, it is known that skin thickness in humans is greater in males than females, whereas the subcutaneous fat thickness is greater in females. In another example, collagen density, the packing of fibrils in the dermis, is higher in the forearms of males than females. See S. Schuster, M. Black, E. McVitie, Br. J. Dermatol, v. 93, pp. 639 to 643, (1975).
Dynamic Properties of the Skin
While knowledge of and use of the properties of the skin, high instrument sensitivity, and compensation for inherent nonlinearities are all important for the application of noninvasive technologies to noninvasive tissue analyte measurements, an understanding of biological and chemical mechanisms that lead to time dependent changes in the properties of skin tissue is equally important and, yet, largely ignored. At a given measurement site, skin tissue is often assumed to be static except for changes in the target analyte and other interfering species. However, variations in the physiological state and fluid distribution of tissue profoundly affect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations are often dominated by fluid compartment equalization through water shifts and are related to hydration levels and changes in blood analyte concentrations.
Total body water accounts for over 60% of the weight of the average person and is distributed between two major compartments: the intracellular fluid (two-thirds of total body water) and the extracellular fluid (one-third of total body water). See A. Guyton, J. Hall, Textbook of Medical of Physiology, 9th ed., Philadelphia, W.B. Saunders Company (1996). The extracellular fluid, in turn, is divided into the interstitial fluid, which is extravascular, and the blood plasma, which is intravascular. Water permeable lipid membranes separate the compartments and water is transferred rapidly between them through the process of diffusion to equalize the concentrations of water and other analytes across the membrane. The net water flux from one compartment to another constitutes the process of osmosis, and the amount of pressure required to prevent osmosis is termed the osmotic pressure. Under static physiological conditions the fluid compartments are at equilibrium. However, during a net fluid gain or loss as a result of water intake or loss, all compartments gain or lose water proportionally and maintain a constant relative volume.
An important mechanism for distributing substances contained in blood serum that are needed by the tissues, for example water and glucose, is through the process of diffusion. It is seen from Fick's Law that diffusion drives the short-term intra-/extra-vascular fluid compartment balance. The movement of water and other analytes from intravascular to extravascular compartments occurs rapidly as molecules of water and other constituents, including glucose, in constant thermal motion, diffuse back and forth through the capillary wall. On average, the rate at which water molecules diffuse through the capillary membrane is about eighty times greater than the rate at which the plasma itself flows linearly along the capillary. In the Fick's Law expression, the actual diffusion flux, IOA, is proportional to the concentration gradient, dC/dx between the two compartments and the diffusivity of the molecule, DA according to the equation
                              I          OA                =                              D            A                    ⁢                    ⁢                                    ⅆ              C                                      ⅆ              x                                ⁢                                              (        1        )            
Short-term increases (or decreases) in blood glucose concentrations lead to an increase (or decrease) in blood osmolality (number of molecules per unit mass of water). Fluid is rapidly re-distributed accordingly and results in a change in the water concentration of each body compartment. In the case of hyperglycemia, the osmotic effect leads to a movement of extravascular water to the intravascular space compartment where glucose concentrations are higher. At the same time, glucose is transported from the intravascular space to the extravascular compartment in an effort to equilibrate the osmolality of the two compartments. Conversely, a decrease in blood glucose concentration leads to a movement of water to extravascular space from the intravascular compartment, along with the movement of glucose from the extravascular space into the intravascular space.
Because the cell membrane is relatively impermeable to most solutes but highly permeable to water, whenever there is a higher concentration of a solute on one side of the cell membrane, water diffuses across the membrane toward the region of higher solute concentration. Large osmotic pressures can develop across the cell membrane with relatively small changes in the concentration of solutes in the extracellular fluid. As a result, relatively small changes in concentration of impermeable solutes in the extracellular fluid, such as glucose, can cause tremendous changes in cell volume.
Sampling Error
Noninvasive measurement of tissue properties and analytes, such as blood glucose concentration, may employ near-infrared (near-IR) spectroscopic methods. S. Malin, T. Ruchti, supra, describes a system for noninvasively estimating blood glucose concentrations in-vivo, using near-infrared spectral analysis. Such near-infrared spectroscopy-based methods use calibrations that are developed using repeated in-vivo optical samples of the same tissue volume. Repeatability of these successive measurements is needed to produce a usable calibration. As herein described, the heterogeneous and dynamic nature of living human skin leads to sampling uncertainty in the in-vivo measurement. Sampling differences can arise due to variable chemical composition and light scattering properties in tissue. As an example: because glucose is not uniformly distributed in tissue, a variation in the volume of tissue sampled is likely to lead to a variation in the strength of the glucose signal, even though glucose concentration in the tissue or blood remains constant.
Variation in the repeated placement of the optical probe used for sampling at the measuring surface site can lead to sampling errors in two separate ways. First, variations in the location of the probe can cause a different tissue volume to be sampled and, second, varying the amount of pressure applied by the probe on the tissue can alter the optical scattering by the tissue, thereby changing the sampled tissue volume. A change in optical sampling may lead to a variation in the spectral signal for a target analyte, even though the concentration of the analyte in the blood or tissue remains unchanged. Furthermore, air gaps between the surface of the optical probe and the surface of the tissue being sampled give rise to variable surface reflection. Variable surface reflection leads to a variable light launch into the tissue that, in turn, gives rise to an increase in nonlinear nature of the spectral measurements. Certainly, a variable nonlinear measurement is difficult to calibrate.
Various systems for guiding and coupling optical probes are known. For example, M. Rondeau, High precision fiber optic alignment spring receptacle and fiber optic probe, U.S. Pat. No. 5,548,674 (Aug. 20, 1996) and R. Rickenbach and R. Boyer, Fiber Optic Probe, U.S. Pat. No. 5,661,843 (Aug. 26, 1997) both disclose fiber optic probe guides using ferrules through which a fiber optic cable or thread is longitudinally threaded. Both devices are connectors that couple fiber optic cables or threads to receptacles in various forms of medical equipment, or to other fiber optic cables. Neither device provides a means for repeatably coupling a fiber optic probe to a tissue measurement site.
T. Kordis, J. Jackson, and J. Lasersohn, Systems using guide sheaths for introducing, deploying and stabilizing cardiac mapping and ablation probes, U.S. Pat. No. 5,636,634 (Jun. 10, 1997) describe a system that employs catheters and guide sheaths to guide cardiac mapping and ablation probes into the chambers of the heart during surgery or diagnostic procedures. The Kordis teachings are directed to surgical methods for the heart, and have nothing to do with optical sampling of tissue in-vivo. Furthermore, the apparatus of Kordis et al. is not suitable for repeatably coupling an optical probe to a tissue measurement site.
M. Kanne, Laser mount positioning device and method of using the same, U.S. Pat. No. 5,956,150 (Sep. 21, 1999) describes a method for using an illumination device, such as a laser to align two components during an assembly process. The Kanne teachings are directed to a manufacturing process rather than optical sampling of tissue in-vivo. The Kanne device does not provide any means for repeatably placing a probe guide at a tissue measurement site. It also lacks means for monitoring the surface temperature at a tissue measurement site and for minimizing surface temperature fluctuations and accumulation of moisture at a tissue measurement site.
D. Kittell, G. Hayes, and P. DeGroot, Apparatus for coupling an optical fiber to a structure at a desired angle, U.S. Pat. No. 5,448,662 (Sep. 5, 1995) disclose an optical fiber support that is coupled to a frame for positioning an optical fiber at a desired angular position. As with the prior art previously described, the teachings of Kittell et al. have nothing to do with optical sampling of tissue in-vivo. Furthermore, the disclosed device allows an operator to immobilize an optical fiber so that it is maintained in a fixed position, but it lacks means of repeatably coupling a fiber optic probe to a tissue measurement site. It also has lacks means for monitoring the surface temperature at a tissue measurement site and for minimizing accumulated moisture and temperature fluctuations at the site.
R. Messerschmidt, Method for non-invasive blood analyte measurement with improved optical interface, U.S. Pat. No. 5,655,530 (Aug. 12, 1997) discloses an index-matching medium to improve the interface between a sensor probe and a skin surface during spectrographic analysis. Messerschmidt teaches a medium containing perfluorocarbons and chlorofluorocarbons. Because they are known carcinogens, chlorofluorocarbons (CFC's) are unsuitable for use in preparations to be used on living tissue. Furthermore, use of CFC's poses a well-known environmental risk. Additionally, Messerschmidt's interface medium is formulated with substances that are likely to leave artifacts in spectroscopic measurements.
E. Ashibe, Measuring condition setting jig, measuring condition setting method and biological measuring system, U.S. Pat. No. 6,381,489 (Apr. 30, 2002) describes a measurement condition setting fixture secured to a measurement site, such as a living body, prior to measurement. At time of measurement, a light irradiating section and light receiving section of a measuring optical system are attached to the setting fixture to attach the measurement site to the optical system. Ashibe does not describe a flexible jig, a two part jig, a jig with a flexible component, or a jig with movable parts. Further, Ashibe does not describe rotational control of a sample probe relative to the sample for a symmetrical or one piece probe tip.
There exists, therefore, a need in the art for a means of achieving the precise optical sampling necessary for developing noninvasive calibrations for measuring tissue analytes.
A solution to the problem of controlling optical sampling during a noninvasive measurement must address several challenges posed by the structural characteristics and dynamic properties of living tissue, such as:                Controlling surface reflection due to optical aberrations in surface coupling and stretching of the surface tissue;        Controlling variations in tissue volume sampled due to imprecise placement;        Controlling variable stretching of dermal collagen, leading to sampling volume uncertainty;        Correcting measurement bias related to water pooling in the tissue resulting from pressure on the area in the vicinity of the measurement site from instrumentation or placement guides; and        Stabilizing hydration of surface tissue.        
It is desirable to provide:                A placement guide for an optical probe that couples the probe to a tissue measurement site for in-vivo optical sampling of the tissue in a fashion allowing increased precision and accuracy of noninvasive analyte concentration estimations;        A means of assuring that the same tissue sample volume is repeatably sampled, thus eliminating sampling errors due to mechanical tissue distortion and probe placement;        A way to minimize temperature fluctuations and stabilize stratum corneum moisture content at the tissue measurement site, thus eliminating further sources of sampling error;        An optical coupling medium to provide a constant interface between an optical probe and the skin at a tissue measurement site that is non-toxic and non-irritating and that does not introduce error into spectroscopic measurements;        A means of monitoring surface temperature at the tissue measurement site, therefore assuring that the temperature remains constant across repeated optical samples; and        A means for correcting the tissue sampling bias that results from the uncertainty inherent to the mechanical attachment process used to install the placement guide at the measurement site.        
Currently, no device using near-infrared spectroscopy for the noninvasive measurement of glucose is in use by persons with diabetes due to technology limitations that include sampling problems, calibration bias, short and long-term reproducibility, and stability. Further, current reported versions of noninvasive glucose concentration analyzers do not consistently yield accurate estimations of glucose concentrations in long-term patient trials in the hands of a typical user or professional operator due, in part, to usability issues. There exists, therefore, a long-felt need for a noninvasive approach to the estimation of glucose concentration that provides long-term accurate and precise glucose concentration estimations in a semi-continuous fashion. Therefore, it is of great benefit to simplify the sampling process and to add controls that enhance precision and accuracy of glucose concentration estimations in the hands of a lay user or professional. Clearly, a guide would be beneficial to a noninvasive optical measuring system allowing tighter control of the sampling and environmental conditions.